Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data

Liu, Qi; Ullah, Hidayat; Wan, Wanggen; Peng, Zhangyou; Hou, Li; Rizvi, Sanam Shahla; Haidery, Saqib Ali; Qu, Tong; Muzahid, Abdullah

doi:10.3390/electronics9061028

Cited by 18 publications

(9 citation statements)

References 59 publications

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“…For modeling spatial densities, the KDE method has also been used in fields such as health, marketing, and environment [ 19 , 21 , 27 ]. KDE has been used in analyzing the patterns of visitors in green parks [ 25 , 28 – 32 ].…”

Section: Sequential Modelmentioning

confidence: 99%

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Hou

Liu

Nebhen

et al. 2021

Computational and Mathematical Methods in Medicine

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The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.

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Section: Sequential Modelmentioning

confidence: 99%

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Hou

Liu

Nebhen

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Weibo users can share their locations in real time online. Weibo check-in data have been used as a proxy to measure actual visits to parks in China [40].…”

Section: Introductionmentioning

confidence: 99%

Patterns of Urban Green Space Use Applying Social Media Data: A Systematic Literature Review

Zabelskyte

Kabisch

Stasiškienė

2022

Land

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Scientific interest in the potential of urban green spaces, particularly urban parks, to improve health and well-being is increasing. Traditional research methods such as observations and surveys have recently been complemented by the use of social media data to understand park visitation patterns. We aimed to provide a systematic overview of how social media data have been applied to identify patterns of urban park use, as well as the advantages and limitations of using social media data in the context of urban park studies. We used the PRISMA method to conduct a systematic literature analysis. Our main findings show that the 22 eligible papers reviewed mainly used social media data to analyse urban park visitors’ needs and demands, and to identify essential park attributes, popular activities, and the spatial, social, and ecological coherence between visitors and parks. The review allowed us to identify the advantages and limitations of using social media data in such research. These advantages include a large database, real-time data, and cost and time savings in data generation of social media data. The identified limitations of using social media data include potentially biased information, a lack of socio-demographic data, and privacy settings on social media platforms. Given the identified advantages and limitations of using social media data in researching urban park visitation patterns, we conclude that the use of social media data as supplementary data constitutes a significant advantage. However, we should critically evaluate the possible risk of bias when using social media data.

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“…Because check-ins are executed by a moderately small group of Weibo users, they have been used in other studies as a good pointer of Weibo users' day-to-day activity [ 71 ] However, our findings reveal that the number of locations has not been increased significantly in Shanghai. When compared to prior studies, it can be noted that all of the studies show that female consumers are much more active than males during the various activities in Shanghai [ 69 – 74 , 82 , 83 ].…”

Section: Discussionmentioning

confidence: 90%

“…KDE has been used in studying the patterns of visitors in green parks [66,[69][70][71][72][73][74][75][76][77]. ese studies, mostly concerned with the visitors' number, are based on the assumption that people who visit a green park have similar behavior.…”

Section: Spatialmentioning

confidence: 99%

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

Ullah

Hameed

Rizvi

et al. 2022

Computational Intelligence and Neuroscience

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There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study’s major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

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Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data

Cited by 18 publications

References 59 publications

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Patterns of Urban Green Space Use Applying Social Media Data: A Systematic Literature Review

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

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